Deep Learning methods are replacing traditional software methods in solving real-world problems.
Cheap and easily available computational power combined with labeled big datasets enabled deep learning algorithms to show their full potential.
AlexNet paper(2012; Krizhevsky et al.[1]}) showed feeding sufficient data into deep neural networks successfully learned to extract representations better than
handcrafted features which let the start an era known as the rise of Deep Learning.
Their great success in solving otherwise hard engineering problems such as
object detection , voice recognition, chatbots, robotic manipulation and autonomous systems
shown they can be applied to various fields thanks to their generalisation capability.[2]}
